Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Numeric7
Text1
Categorical6

Alerts

RowNumber is uniformly distributedUniform
RowNumber has unique valuesUnique
CustomerId has unique valuesUnique
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2025-12-15 06:24:23.904767
Analysis finished2025-12-15 06:24:34.058320
Duration10.15 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

RowNumber
Real number (ℝ)

Uniform  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:34.170780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2025-12-15T06:24:34.301608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99841
 
< 0.1%
99831
 
< 0.1%
99821
 
< 0.1%
99811
 
< 0.1%
99801
 
< 0.1%
99791
 
< 0.1%
99781
 
< 0.1%
99771
 
< 0.1%
99761
 
< 0.1%
99751
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
99991
< 0.1%
99981
< 0.1%
99971
< 0.1%
99961
< 0.1%
99951
< 0.1%
99941
< 0.1%
99931
< 0.1%
99921
< 0.1%
99911
< 0.1%

CustomerId
Real number (ℝ)

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:34.445375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2025-12-15T06:24:34.587881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156567101
 
< 0.1%
157681631
 
< 0.1%
156727541
 
< 0.1%
157192761
 
< 0.1%
156926641
 
< 0.1%
157035631
 
< 0.1%
155799691
 
< 0.1%
156560621
 
< 0.1%
156662951
 
< 0.1%
156954741
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
155657011
< 0.1%
155657061
< 0.1%
155657141
< 0.1%
155657791
< 0.1%
155657961
< 0.1%
155658061
< 0.1%
155658781
< 0.1%
155658791
< 0.1%
155658911
< 0.1%
155659961
< 0.1%
ValueCountFrequency (%)
158156901
< 0.1%
158156601
< 0.1%
158156561
< 0.1%
158156451
< 0.1%
158156281
< 0.1%
158156261
< 0.1%
158156151
< 0.1%
158155601
< 0.1%
158155521
< 0.1%
158155341
< 0.1%

Surname
Text

Distinct2932
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:34.977741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length16
Mean length6.4349
Min length2

Characters and Unicode

Total characters64349
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)15.6%

Sample

1st rowHargrave
2nd rowHill
3rd rowOnio
4th rowBoni
5th rowMitchell
ValueCountFrequency (%)
lo33
 
0.3%
smith32
 
0.3%
scott29
 
0.3%
martin29
 
0.3%
walker28
 
0.3%
brown26
 
0.3%
genovese25
 
0.2%
shih25
 
0.2%
yeh25
 
0.2%
wright24
 
0.2%
Other values (2931)9779
97.3%
2025-12-15T06:24:35.462379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64349
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5799
 
9.0%
e5764
 
9.0%
n5235
 
8.1%
o4905
 
7.6%
i4491
 
7.0%
r3547
 
5.5%
l2921
 
4.5%
s2592
 
4.0%
u2552
 
4.0%
h2150
 
3.3%
Other values (45)24393
37.9%

CreditScore
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:35.591684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2025-12-15T06:24:35.720992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850233
 
2.3%
67863
 
0.6%
65554
 
0.5%
70553
 
0.5%
66753
 
0.5%
68452
 
0.5%
65150
 
0.5%
67050
 
0.5%
68348
 
0.5%
65248
 
0.5%
Other values (450)9296
93.0%
ValueCountFrequency (%)
3505
0.1%
3511
 
< 0.1%
3581
 
< 0.1%
3591
 
< 0.1%
3631
 
< 0.1%
3651
 
< 0.1%
3671
 
< 0.1%
3731
 
< 0.1%
3762
 
< 0.1%
3821
 
< 0.1%
ValueCountFrequency (%)
850233
2.3%
8498
 
0.1%
8485
 
0.1%
8476
 
0.1%
8465
 
0.1%
8456
 
0.1%
8447
 
0.1%
8432
 
< 0.1%
8427
 
0.1%
84112
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France5014
50.1%
Germany2509
25.1%
Spain2477
24.8%

Length

2025-12-15T06:24:35.865842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:35.954029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
france5014
50.1%
germany2509
25.1%
spain2477
24.8%

Most occurring characters

ValueCountFrequency (%)
n10000
16.7%
a10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n10000
16.7%
a10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n10000
16.7%
a10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n10000
16.7%
a10000
16.7%
r7523
12.5%
e7523
12.5%
F5014
8.4%
c5014
8.4%
G2509
 
4.2%
m2509
 
4.2%
y2509
 
4.2%
S2477
 
4.1%
Other values (2)4954
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male5457
54.6%
Female4543
45.4%

Length

2025-12-15T06:24:39.286011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:39.367885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male5457
54.6%
female4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)49086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e14543
29.6%
a10000
20.4%
l10000
20.4%
M5457
 
11.1%
F4543
 
9.3%
m4543
 
9.3%

Age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:39.471512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2025-12-15T06:24:39.611783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37478
 
4.8%
38477
 
4.8%
35474
 
4.7%
36456
 
4.6%
34447
 
4.5%
33442
 
4.4%
40432
 
4.3%
39423
 
4.2%
32418
 
4.2%
31404
 
4.0%
Other values (60)5549
55.5%
ValueCountFrequency (%)
1822
 
0.2%
1927
 
0.3%
2040
 
0.4%
2153
 
0.5%
2284
0.8%
2399
1.0%
24132
1.3%
25154
1.5%
26200
2.0%
27209
2.1%
ValueCountFrequency (%)
922
 
< 0.1%
881
 
< 0.1%
851
 
< 0.1%
842
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
814
< 0.1%
803
< 0.1%
794
< 0.1%
785
0.1%

Tenure
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:39.720939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2025-12-15T06:24:39.814882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
21048
10.5%
11035
10.3%
71028
10.3%
81025
10.2%
51012
10.1%
31009
10.1%
4989
9.9%
9984
9.8%
6967
9.7%
10490
4.9%
ValueCountFrequency (%)
0413
 
4.1%
11035
10.3%
21048
10.5%
31009
10.1%
4989
9.9%
51012
10.1%
6967
9.7%
71028
10.3%
81025
10.2%
9984
9.8%
ValueCountFrequency (%)
10490
4.9%
9984
9.8%
81025
10.2%
71028
10.3%
6967
9.7%
51012
10.1%
4989
9.9%
31009
10.1%
21048
10.5%
11035
10.3%

Balance
Real number (ℝ)

Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:39.938758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2025-12-15T06:24:40.095029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03617
36.2%
130170.822
 
< 0.1%
105473.742
 
< 0.1%
159397.751
 
< 0.1%
144238.71
 
< 0.1%
112262.841
 
< 0.1%
109106.81
 
< 0.1%
142147.321
 
< 0.1%
109109.331
 
< 0.1%
146587.31
 
< 0.1%
Other values (6372)6372
63.7%
ValueCountFrequency (%)
03617
36.2%
3768.691
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
16893.591
 
< 0.1%
23503.311
 
< 0.1%
24043.451
 
< 0.1%
27288.431
 
< 0.1%
27517.151
 
< 0.1%
27755.971
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
238387.561
< 0.1%
222267.631
< 0.1%
221532.81
< 0.1%
216109.881
< 0.1%
214346.961
< 0.1%
213146.21
< 0.1%
212778.21
< 0.1%
212696.321
< 0.1%
212692.971
< 0.1%

NumOfProducts
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Length

2025-12-15T06:24:40.218723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:40.301160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring characters

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Length

2025-12-15T06:24:40.399983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:40.474478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring characters

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Length

2025-12-15T06:24:40.560938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:40.632959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring characters

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

EstimatedSalary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:24:40.728518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2025-12-15T06:24:40.877604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
121505.611
 
< 0.1%
89874.821
 
< 0.1%
72500.681
 
< 0.1%
182692.81
 
< 0.1%
4993.941
 
< 0.1%
124964.821
 
< 0.1%
161971.421
 
< 0.1%
3729.891
 
< 0.1%
55313.441
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
11.581
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
106.671
< 0.1%
123.071
< 0.1%
142.811
< 0.1%
143.341
< 0.1%
178.191
< 0.1%
216.271
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199953.331
< 0.1%
199929.171
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199808.11
< 0.1%
199805.631
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7963 
1
2037 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Length

2025-12-15T06:24:41.026607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:24:41.100548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring characters

ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Interactions

2025-12-15T06:24:32.407493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:25.736403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:27.289107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:28.943582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.694048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.501011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.275505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:32.571820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:25.964325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:27.567728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.053657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.814955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.606144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.444427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:32.749607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:26.171196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:27.818208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.160551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.925355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.709680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.605615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:32.909984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:26.417659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:28.073436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.257481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.035597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.810868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.753913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:33.096432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:26.651777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:28.299708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.365412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.150384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.919724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.916725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:33.267491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:26.857842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:28.508172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.471510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.258712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.027905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:32.070913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:33.438513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:27.083374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:28.760345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:29.577665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:30.370728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:31.134548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:24:32.235611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-15T06:24:41.173630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBalanceCreditScoreCustomerIdEstimatedSalaryExitedGenderGeographyHasCrCardIsActiveMemberNumOfProductsRowNumberTenure
Age1.0000.033-0.0080.009-0.0020.3750.0260.0500.0130.1440.0870.000-0.010
Balance0.0331.0000.006-0.0140.0120.1410.0000.3150.0390.0140.230-0.009-0.010
CreditScore-0.0080.0061.0000.0060.0010.0860.0000.0180.0000.0250.0170.0050.001
CustomerId0.009-0.0140.0061.0000.0150.0230.0000.0000.0000.0110.0060.004-0.015
EstimatedSalary-0.0020.0120.0010.0151.0000.0000.0210.0170.0000.0250.019-0.0060.008
Exited0.3750.1410.0860.0230.0001.0000.1060.1730.0000.1560.3870.0000.022
Gender0.0260.0000.0000.0000.0210.1061.0000.0220.0000.0200.0420.0000.025
Geography0.0500.3150.0180.0000.0170.1730.0221.0000.0050.0180.0470.0180.028
HasCrCard0.0130.0390.0000.0000.0000.0000.0000.0051.0000.0060.0000.0080.026
IsActiveMember0.1440.0140.0250.0110.0250.1560.0200.0180.0061.0000.0380.0000.021
NumOfProducts0.0870.2300.0170.0060.0190.3870.0420.0470.0000.0381.0000.0090.035
RowNumber0.000-0.0090.0050.004-0.0060.0000.0000.0180.0080.0000.0091.000-0.007
Tenure-0.010-0.0100.001-0.0150.0080.0220.0250.0280.0260.0210.035-0.0071.000

Missing values

2025-12-15T06:24:33.727905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-15T06:24:33.956816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
0115634602Hargrave619FranceFemale4220.00111101348.881
1215647311Hill608SpainFemale41183807.86101112542.580
2315619304Onio502FranceFemale428159660.80310113931.571
3415701354Boni699FranceFemale3910.0020093826.630
4515737888Mitchell850SpainFemale432125510.8211179084.100
5615574012Chu645SpainMale448113755.78210149756.711
6715592531Bartlett822FranceMale5070.0021110062.800
7815656148Obinna376GermanyFemale294115046.74410119346.881
8915792365He501FranceMale444142051.0720174940.500
91015592389H?684FranceMale272134603.8811171725.730
RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExited
9990999115798964Nkemakonam714GermanyMale33335016.6011053667.080
9991999215769959Ajuluchukwu597FranceFemale53488381.2111069384.711
9992999315657105Chukwualuka726SpainMale3620.00110195192.400
9993999415569266Rahman644FranceMale287155060.4111029179.520
9994999515719294Wood800FranceFemale2920.00200167773.550
9995999615606229Obijiaku771FranceMale3950.0021096270.640
9996999715569892Johnstone516FranceMale351057369.61111101699.770
9997999815584532Liu709FranceFemale3670.0010142085.581
9998999915682355Sabbatini772GermanyMale42375075.3121092888.521
99991000015628319Walker792FranceFemale284130142.7911038190.780